Cristina Molero-Río

Cristina Molero-Río
Universidad de Sevilla | US · Instituto Universitario de Investigación Matemática

PhD Student at Universidad de Sevilla

About

13
Publications
8,401
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138
Citations
Citations since 2016
13 Research Items
136 Citations
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201620172018201920202021202201020304050

Publications

Publications (13)
Preprint
Full-text available
In this paper, we tailor optimal randomized regression trees to handle multivariate functional data. A compromise between prediction accuracy and sparsity is sought. Whilst fitting the tree model, the detection of a reduced number of intervals that arecritical for prediction, as well as the control of their length, is performed. Local and global sp...
Article
In this paper, we model an optimal regression tree through a continuous optimization problem, where a compromise between prediction accuracy and both types of sparsity, namely local and global, is sought. Our approach can accommodate important desirable properties for the regression task, such as cost-sensitivity and fairness. Thanks to the smoothn...
Article
Full-text available
Since the seminal paper by Bates and Granger in 1969, a vast number of ensemble methods that combine different base regressors to generate a unique one have been proposed in the literature. The so-obtained regressor method may have better accuracy than its components, but at the same time it may overfit, it may be distorted by base regressors with...
Article
Full-text available
Classification and regression trees, as well as their variants, are off-the-shelf methods in Machine Learning. In this paper, we review recent contributions within the Continuous Optimization and the Mixed-Integer Linear Optimization paradigms to develop novel formulations in this research area. We compare those in terms of the nature of the decisi...
Article
Full-text available
Classification and Regression Trees (CARTs) are off-the-shelf techniques in modern Statistics and Machine Learning. CARTs are traditionally built by means of a greedy procedure, sequentially deciding the splitting predictor variable(s) and the associated threshold. This greedy approach trains trees very fast, but, by its nature, their classificatio...
Preprint
Full-text available
Classification and regression trees, as well as their variants, are off-the-shelf methods in Machine Learning. In this paper, we review recent contributions within the Continuous Optimization and the Mixed-Integer Linear Optimization paradigms to develop novel formulations in this research area. We compare those in terms of the nature of the decisi...
Preprint
Full-text available
Since the seminal paper by Bates and Granger in 1969, a vast number of ensemble methods that combine different base regressors to generate a unique one have been proposed in the literature. The so-obtained regressor method may have better accuracy than its components , but at the same time it may overfit, it may be distorted by base regressors with...
Preprint
Full-text available
In this paper, we model an optimal regression tree through a continuous optimization problem, where a compromise between prediction accuracy and both types of sparsity, namely local and global, is sought. Our approach can accommodate important desirableproperties for the regression task, such as cost-sensitivity and fairness. Thanks to thesmoothnes...
Preprint
Full-text available
COVID-19 is an infectious disease that was first identified in China in December 2019. Subsequently COVID-19 started to spread broadly, to also arrive in Spain by the end of Jan-uary 2020. This pandemic triggered confinement measures, in order to reduce the expansion of the virus so as not to saturate the health care system. With the aim of providi...
Preprint
Decision trees are popular Classification and Regression tools and, when small-sized, easy to interpret. Traditionally, a greedy approach has been used to build the trees, yielding a very fast training process; however, controlling sparsity (a proxy for interpretability) is challenging. In recent studies, optimal decision trees, where all decisions...
Article
Full-text available
Decision trees are popular Classification and Regression tools and, when small-sized, easy to interpret. Traditionally, a greedy approach has been used to build the trees, yielding a very fast training process; however, controlling sparsity (a proxy for interpretability) is challenging. In recent studies, optimal decision trees, where all decisions...
Preprint
Full-text available
Decision trees are popular Regression and Classification tools, easy to interpret and with excellent performance. The training process is very fast, since a greedy approach is used to build the tree. In recent studies, optimal decision trees, where all decisions are optimized simultaneously, have shown a better learning performance. In this paper,...
Preprint
Full-text available
Classification and Regression Trees (CARTs) are an off-the-shelf technique in modern Statistics and Machine Learning. CARTs are traditionally built by means of a greedy procedure, sequentially deciding the splitting predictor variable(s) and the associated threshold. This greedy approach trains trees very fast, but, by its nature, the classificatio...

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Projects

Project (1)
Project
NeEDS (Network of European Data Scientists) provides an integrated modelling and computing environment that facilitates data analysis and data visualization to enhance interaction. NeEDS brings together an excellent interdisciplinary research team that integrates expertise from three relevant academic disciplines, Mathematical Optimization, Visualization and Network Science, and is excellently placed to tackle the challenges. NeEDS develops mathematical models, yielding results which are interpretable, easy-to-visualize, and flexible enough to incorporate user knowledge from complex data. These models require the numerical resolution of computationally demanding Mixed Integer Nonlinear Programming formulations, and for this purpose NeEDS develops innovative mathematical optimization based heuristics.